A review of artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
Time series analysis with explanatory variables: A systematic literature review
Time series analysis with explanatory variables encompasses methods to model and predict
correlated data taking into account additional information, known as exogenous variables. A …
correlated data taking into account additional information, known as exogenous variables. A …
Forecasting peak air pollution levels using NARX models
Air pollution has a negative impact on human health. For this reason, it is important to
correctly forecast over-threshold events to give timely warnings to the population. Nonlinear …
correctly forecast over-threshold events to give timely warnings to the population. Nonlinear …
Ensemble method based on Artificial Neural Networks to estimate air pollution health risks
Estimating of daily hospital admissions due to air pollution is a leading issue in
environmental science. To better understand this problem, it is essential to improve the …
environmental science. To better understand this problem, it is essential to improve the …
A hybrid deep learning framework for urban air quality forecasting
Deep learning models address air quality forecasting problems far more effectively and
efficiently than the traditional machine learning models. Specifically, Long Short-Term …
efficiently than the traditional machine learning models. Specifically, Long Short-Term …
Air quality assessment using a weighted Fuzzy Inference System
Air pollution is a current monitored problem in areas with high population density such as
big cities. In this sense, environmental modelling should be accurate in order to generate …
big cities. In this sense, environmental modelling should be accurate in order to generate …
Forecasting PM2.5 in Malaysia Using a Hybrid Model
Predicting future PM2. 5 concentrations based on knowledge obtained from past
observational data is very useful for predicting air pollution. This paper aims to develop a …
observational data is very useful for predicting air pollution. This paper aims to develop a …
Prediction of hourly ground-level PM2. 5 concentrations 3 days in advance using neural networks with satellite data in eastern China
X Mao, T Shen, X Feng - Atmospheric Pollution Research, 2017 - Elsevier
This study is an attempt to explore the effectiveness of satellite data in predicting hourly PM
2.5 (Respirable particulate matter with aerodynamic diameter below 2.5 μm) concentrations …
2.5 (Respirable particulate matter with aerodynamic diameter below 2.5 μm) concentrations …
Prediction of hourly O3 concentrations using support vector regression algorithms
In this paper we present an application of the Support Vector Regression algorithm (SVMr)
to the prediction of hourly ozone values in Madrid urban area. In order to improve the …
to the prediction of hourly ozone values in Madrid urban area. In order to improve the …
Hybridization of air quality forecasting models using machine learning and clustering: An original approach to detect pollutant peaks
This paper presents an original approach combining Artificial Neural Networks (ANNs) and
clustering in order to detect pollutant peaks. We developed air quality forecasting models …
clustering in order to detect pollutant peaks. We developed air quality forecasting models …